Strategic interdependence, governance effectiveness and supplier performance: A dyadic case study investigation and theory development
Bibliographic record
Abstract
Abstract Inter‐organizational exchange governance approaches are often characterized as two broad types: relational and transactional. However, in fast changing business contexts, the contextual contingencies do not present ideal conditions for practicing purely relational or transactional approach. Understanding the dynamic of key contextual factors and their effects on a firm's resource capabilities and inter‐organizational power structure is crucial for identifying the appropriate governance structure over time. In this paper we explore the exchanges between an OEM and five of its strategic suppliers that operate in high‐end, short product life cycle motorbike industry, to understand the key contextual factors and the relationships among business context, governance structure, and exchange practices in a dyadic context. It is observed that firms deviate from the conventional choices of either transactional or relational governance to a combination of contractual and relational aspects to make the governance structure effective. Based on case studies, a theoretical framework is proposed to explain the rationale, feasibility and effectiveness of combining contractual and relational aspects in different contexts. The framework suggests that the degree of strategic interdependence between the trading partners fundamentally drives the effectiveness of governance structure and exchange practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".